Design of Concession and Annual Payments for Availability Payment Public Private Partnership (PPP) Projects
Bibliographic record
Abstract
Public Private Partnerships (PPPs) have emerged as an important project delivery method in the United States, where funding agencies are finding it difficult to support the increasing demand of highway projects. The United States has witnessed several types of PPPs during the past two decades, and a recent trend shows that newer designs of PPPs are being adopted for upcoming projects. Availability Payment, an extensively used PPP in the United Kingdom and Canada, is the newest performancebased PPP implemented in California and Florida. Extensive use of these PPPs in other countries strongly supports the belief of their widespread acceptance in the United States. The literature review indicates that concession term and availability payments are the most important parameters of this PPP. However, the public agencies do not have any solid tool that can design these parameters and have to largely depend on traditional methods. This research work introduces a hybrid model that will allow the public sector to determine the upper limit of availability payments and concession duration. The hybrid model has been developed by combining the stochastic dynamic programming model with multi-objective optimization principles. The model allows using private sector's financial condition, uncertainty of private sector's performance and the remaining life cycle costs of the asset. The use of this model ensures cost savings for the public sector and financial stability for the private sector simultaneously. This research includes an analysis of the CALTRANS' Presidio Parkway Project as a case study to demonstrate the use of the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".